Project description:While the application of cryogenic electron microscopy (cryo-EM) to helical polymers in biology has a long history, due to the huge number of helical macromolecular assemblies in viruses, bacteria, archaea, and eukaryotes, the use of cryo-EM to study synthetic soft matter noncovalent polymers has been much more limited. This has mainly been due to the lack of familiarity with cryo-EM in the materials science and chemistry communities, in contrast to the fact that cryo-EM was developed as a biological technique. Nevertheless, the relatively few structures of self-assembled peptide nanotubes and ribbons solved at near-atomic resolution by cryo-EM have demonstrated that cryo-EM should be the method of choice for a structural analysis of synthetic helical filaments. In addition, cryo-EM has also demonstrated that the self-assembly of soft matter polymers has enormous potential for polymorphism, something that may be obscured by techniques such as scattering and spectroscopy. These cryo-EM structures have revealed how far we currently are from being able to predict the structure of these polymers due to their chaotic self-assembly behavior.
Project description:Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https://github.com/JachyLikeCoding/UPicker.
Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.
Project description:Alzheimer's disease is the most common neurodegenerative disease, and there are no mechanism-based therapies. The disease is defined by the presence of abundant neurofibrillary lesions and neuritic plaques in the cerebral cortex. Neurofibrillary lesions comprise paired helical and straight tau filaments, whereas tau filaments with different morphologies characterize other neurodegenerative diseases. No high-resolution structures of tau filaments are available. Here we present cryo-electron microscopy (cryo-EM) maps at 3.4-3.5 Å resolution and corresponding atomic models of paired helical and straight filaments from the brain of an individual with Alzheimer's disease. Filament cores are made of two identical protofilaments comprising residues 306-378 of tau protein, which adopt a combined cross-β/β-helix structure and define the seed for tau aggregation. Paired helical and straight filaments differ in their inter-protofilament packing, showing that they are ultrastructural polymorphs. These findings demonstrate that cryo-EM allows atomic characterization of amyloid filaments from patient-derived material, and pave the way for investigation of a range of neurodegenerative diseases.
Project description:Filament assembly of amyloid-β peptides ending at residue 42 (Aβ42) is a central event in Alzheimer’s disease. Here, we report the cryo–electron microscopy (cryo-EM) structures of Aβ42 filaments from human brains. Two structurally related S-shaped protofilament folds give rise to two types of filaments. Type I filaments were found mostly in the brains of individuals with sporadic Alzheimer’s disease, and type II filaments were found in individuals with familial Alzheimer’s disease and other conditions. The structures of Aβ42 filaments from the brain differ from those of filaments assembled in vitro. By contrast, in AppNL-F knock-in mice, Aβ42 deposits were made of type II filaments. Knowledge of Aβ42 filament structures from human brains may lead to the development of inhibitors of assembly and improved imaging agents.
Project description:Prion protein (PrP) aggregation and formation of PrP amyloid (APrP) are central events in the pathogenesis of prion diseases. In the dominantly inherited prion protein amyloidosis known as Gerstmann-Sträussler-Scheinker (GSS) disease, plaques made of PrP amyloid are present throughout the brain. The c.593t > c mutation in the prion protein gene (PRNP) results in a phenylalanine to serine amino acid substitution at PrP residue 198 (F198S) and causes the most severe amyloidosis among GSS variants. It has been shown that neurodegeneration in this disease is associated with the presence of extracellular APrP plaques and neuronal intracytoplasmic Tau inclusions, that have been shown to contain paired helical filaments identical to those found in Alzheimer disease. Using cryogenic electron microscopy (cryo-EM), we determined for the first time the structures of filaments of human APrP, isolated post-mortem from the brain of two symptomatic PRNP F198S mutation carriers. We report that in GSS (F198S) APrP filaments are composed of dimeric, trimeric and tetrameric left-handed protofilaments with their protomers sharing a common protein fold. The protomers in the cross-β spines consist of 62 amino acids and span from glycine 80 to phenylalanine 141, adopting a previously unseen spiral fold with a thicker outer layer and a thinner inner layer. Each protomer comprises nine short β-strands, with the β1 and β8 strands, as well as the β4 and β9 strands, forming a steric zipper. The data obtained by cryo-EM provide insights into the structural complexity of the PrP filament in a dominantly inherited human PrP amyloidosis. The novel findings highlight the urgency of extending our knowledge of the filaments' structures that may underlie distinct clinical and pathologic phenotypes of human neurodegenerative diseases.
Project description:Adult individuals with Down syndrome (DS) develop Alzheimer disease (AD). Whether there is a difference between AD in DS and AD regarding the structure of amyloid-β (Aβ) and tau filaments is unknown. Here we report the structure of Aβ and tau filaments from two DS brains. We found two Aβ40 filaments (types IIIa and IIIb) that differ from those previously reported in sporadic AD and two types of Aβ42 filaments (I and II) identical to those found in sporadic and familial AD. Tau filaments (paired helical filaments and straight filaments) were identical to those in AD, supporting the notion of a common mechanism through which amyloids trigger aggregation of tau. This knowledge is important for understanding AD in DS and assessing whether adults with DS could be included in AD clinical trials.
Project description:In structural biology, cryo-electron microscopy (cryo-EM) has emerged as the main technique for determining the atomic structures of macromolecular complexes. This has largely been due to the introduction of direct electron detectors, which have allowed for routinely reaching a near-atomic resolution when imaging such complexes. In chemistry and materials science, the applications of cryo-EM have been much more limited. A recent paper (Z. Li et al., Chemically Controlled Helical Polymorphism In Protein Tubes By Selective Modulation Of Supramolecular Interactions, J. Am. Chem. Soc. 2019, 141, 19448-19457) has used low resolution cryo-EM to analyze polymorphic helical tubes formed by a tetrameric protein, and has made detailed models for the interfaces between the tetramers in these assemblies. Due to intrinsic ambiguities in determining the correct helical symmetry, we show that many of the models are likely to be wrong. This note highlights both the enormous potential for using cryo-EM, and also the pitfalls possible for helical assemblies when a near-atomic level of resolution is not reached.